{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "54787328",
   "metadata": {},
   "source": [
    "# [Learning to solve mixed-integer optimal control problems](https://arxiv.org/abs/2506.19646)\n",
    "\n",
    " A neural control policy is trained by solving a parametric mixed-integer optimal control problem as a differentiable program using [Differentiable predictive control (DPC) framework](https://www.sciencedirect.com/science/article/pii/S0959152422000981). Mixed-integer methodology is adopted from: https://arxiv.org/abs/2506.19646\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0566b798",
   "metadata": {},
   "source": [
    "## NeuroMANCER and Dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b1bee61",
   "metadata": {},
   "source": [
    "### Install (Colab only)\n",
    "Skip this step when running locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "086484b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install neuromancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "efd912f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from neuromancer.system import Node, SystemPreview\n",
    "from neuromancer.modules import blocks\n",
    "from neuromancer.dataset import DictDataset\n",
    "from neuromancer.constraint import variable\n",
    "from neuromancer.loss import PenaltyLoss\n",
    "from neuromancer.problem import Problem\n",
    "from neuromancer.trainer import Trainer\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from torch.nn.functional import gumbel_softmax\n",
    "from neuromancer.modules.activations import activations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28ac0bfd",
   "metadata": {},
   "source": [
    "## System model\n",
    "\n",
    "We consider a simple conceptual thermal energy managment system represented by linear state-space formulation:\n",
    "\n",
    "$$\n",
    "{\\bf x}_{k+1} = {\\bf Ax}_k+ {\\bf B} \\begin{bmatrix}u_k, \\\\ \\delta_k \\end{bmatrix}+{\\bf  E}d_k \\\\\n",
    " A=\\begin{bmatrix}\n",
    "a_1 - \\lambda & \\lambda \\\\\n",
    "\\lambda & a_2-\\lambda\n",
    "\\end{bmatrix},\\;\n",
    "B=\n",
    "\\begin{bmatrix}\n",
    "0.7\\, b_1 & 0 \\\\\n",
    "0.3\\, b_1 & b_2\n",
    "\\end{bmatrix},\\;\n",
    "E=\n",
    "\\begin{bmatrix}\n",
    "-e_1 & 0 \\\\\n",
    "0 & -e_2\n",
    "\\end{bmatrix},\n",
    "$$\n",
    "\n",
    "with system states $x_1$ and $x_2$ representing thermal energy stored in two thermal energy storage tanks, $a_1$ and $a_2$ denote energy dissipation and $\\lambda$ energy transition between the two tanks. Thermal energy is supplied by a heat pump $u\\in\\mathbb{R}$ with thermal efficiency $b_1$ and a fixed $70:30 \\%$ split to $x_1$ and $x_2$, respectively. The second control input $\\delta\\in\\mathbb\\{0,1,2,3,4,5\\}$ can be interpreted as heat rods of thermal efficiency $b_2$, supplying thermal energy to $x_2$. Energy consumption is represented as a disturbance variable $d\\in\\mathbb{R}$, split equally from both storage tanks. \n",
    "\n",
    "The conceputal dynamics model is inspired by examples in:\n",
    "\n",
    " https://arxiv.org/abs/2506.19646 and https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8815030 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8c20d25c",
   "metadata": {},
   "outputs": [],
   "source": [
    "a_1, a_2, _labmda_ = 0.95, 0.85, 0.0020 # model parameters\n",
    "b_1, b_2, e_1, e_2 = 0.2, 0.0825, 0.1, 0.1\n",
    "\n",
    "A = torch.tensor([[a_1 - _labmda_, _labmda_], # state dynamics\n",
    "                [_labmda_, a_2 - _labmda_]])\n",
    "B = torch.tensor([[0.7*b_1, 0],   # input dynamics\n",
    "                    [0.3*b_1, b_2]])\n",
    "E = torch.tensor([[-e_1], [-e_2]]) # disturbance gains\n",
    "\n",
    "nx, nref = 2, 2 # number of states, number of references \n",
    "nu, ndelta, nd = 1, 1, 1 # number of continous inputs, integer inputs, and disturbances\n",
    "\n",
    "dynamics = lambda x, u, d: x @ A.T + u @ B.T + d @ E.T # plant model\n",
    "\n",
    "# process constraints\n",
    "x1_min, x2_min = 0.0, 0.0\n",
    "x1_max, x2_max, input_energy_max = 8, 4, 10\n",
    "u_delta_min, u_delta_max = 0, 5 # integer control input bounds\n",
    "u_c_min, u_c_max = 0.0, 7.0     # continous control input bounds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9d380a8",
   "metadata": {},
   "source": [
    "## Mixed-Integer Differentiable Predictive Control\n",
    "\n",
    "Next we show how to solve the corresponding mixed-integer parametric optimal control problem using the [DPC method](https://www.sciencedirect.com/science/article/pii/S0959152422000981) implemented in Neuromancer.\n",
    "\n",
    "**Differentiable Mixed-Integer Optimal Control Problem**\n",
    "\n",
    "We pose the solution (control decisions) of a parametric mixed-integer optimal control problem as an output of neural control policy $[u_k, \\delta_k] = \\pi_\\theta(\\xi_k)$, parameterized by $\\theta$. In this example the vector of control parameters $\\xi_k$ includes initial condition $x_k$ and future windows of set points and disturbances $[r_k,\\dots,r_{k+N}]$ and $[d_k,\\dots,d_{k+N-1}]$, respectively. \n",
    "\n",
    "The optimal control problem is given as a standard Model Predictive Control formulation with reference tracking $\\!\\|x_k\\!-\\!r_k\\|_Q^2$ and control smoothing objectives $\\|u_{k+1}-u_{k}\\|_R^2$, $ \\|\\delta_{k+1}-\\delta_{k}\\|^2_R$ as:\n",
    "$$\n",
    "\\begin{align}\n",
    "    \\min_{\\theta}  \\, \\!\\mathbb{E}_{  \\xi_k \\sim P_{ \\xi} }\\,&\\!  \n",
    "    \\bigg[\\|x_N\\!-\\!r_N\\|_Q^2\n",
    "       \\!+\\!\\sum_{k=0}^{N-1}\n",
    "       \\!\\|x_k\\!-\\!r_k\\|_Q^2+\n",
    "       \\|u_{k+1}-u_{k}\\|_R^2+\n",
    "       \\|\\delta_{k+1}-\\delta_{k}\\|^2_R\\!\\bigg]  \\\\\n",
    "    \\text{s.t.} \\;\n",
    "    {\\bf x}_{k+1} &= {\\bf Ax}_k+ {\\bf B} \\begin{bmatrix}u_k, \\\\ \\delta_k \\end{bmatrix}+{\\bf  E}d_k  \\\\\n",
    "    [u_k,\\;\\delta_k] & = {\\pi}_{\\theta}(\\xi_k),  \\\\\n",
    "        u_k  \\in \\mathcal{U}&,\\;\\delta_k \\in \\mathcal{D}, \\quad \\forall k\\in\\{0,1,\\dots,N\\!-\\!1\\},  \\\\\n",
    "    x_k  \\in \\mathcal{X}&, \\quad\\quad\\quad\\quad\\;\\; \\forall k\\in\\{0,1,\\dots,N\\},\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "In short, we optimize the parameters of our control policy $\\theta$ such that control objectives are minimized and process constraints $\\mathcal{U}$, $\\mathcal{X}$, and $\\mathcal{D}$ are not violated. State constraints are relaxed as additional terms in loss function via penalty method, while input $\\mathcal{U}$ and integrality $\\mathcal{D}$ constraints are enforced within the computational graph. In this notebook, we show showcase two method for enforcing integrality constraints; via sigmoid relaxed rounding and via gumbel softmax categorical encoding."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b2b6da1",
   "metadata": {},
   "source": [
    "## Constructing the neural control policy\n",
    "\n",
    "Our control policy $\\pi_\\theta(\\cdot)$ consits of two neural modules, one for continous control input and the other for discrete control input.\n",
    "\n",
    "Note that depending on which integrality constraints enforcing method is chosen, output of the neural module varies. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dc5516b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "STE_methods = ['sigmoid', 'softmax']\n",
    "STE_method = STE_methods[0]\n",
    "\n",
    "nsteps = 20 # prediction horizon length    \n",
    "integers = torch.arange(u_delta_min, u_delta_max+.1, dtype=torch.get_default_dtype()).unsqueeze(0) # vector of feasible integers\n",
    "\n",
    "net_continous = blocks.MLP_bounds(insize=nx+(nref+nd)*(nsteps+1), outsize=nu, hsizes=[64,64], # neural module for continous control inputs\n",
    "                            nonlin=activations['gelu'], min=u_c_min, max=u_c_max)\n",
    "\n",
    "int_out_size = integers.shape[-1] if STE_method == 'softmax' else ndelta\n",
    "net_integer = blocks.MLP(insize=nx+(nref+nd)*(nsteps+1), outsize=int_out_size, hsizes=[64,64], # neural module for integer control inputs\n",
    "                            nonlin=activations['gelu'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "285d14ef",
   "metadata": {},
   "source": [
    "## Straight-Through Estimation (STE) Methodology\n",
    "\n",
    "We enable gradient-based optimization by estimating the gradients of discrete, non-differentiable functions through gradient approximations. In this notebook, we showcase two different approaches:\n",
    "\n",
    "1. **STE with sigmoid as a differentiable surrogate**:  \n",
    "   The forward pass is performed by rounding the relaxed solution to the nearest integer using `torch.round(x)`. For the backward pass, we utilize the gradients of a sloped sigmoid function, where a larger slope coefficient (`slope ∈ ℝ⁺`) results in a steeper sigmoid and sharper gradients. Increasing this coefficient may lead to higher accuracy in capturing the mixed-integer optimal control problem. However, it may also introduce numerical instability (exploding gradients).\n",
    "\n",
    "2. **STE with softmax as a differentiable surrogate**:  \n",
    "   The forward pass consists of applying `argmax` to a normalized probability vector (i.e., one-hot encoding), which is then multiplied by a vector of feasible `integers`. For the backward pass, we use the gradients of the `softmax` function, where the probability scores are additionally scaled by a temperature coefficient (`tau ∈ ℝ⁺`). Increasing `tau` promotes a uniform distribution over integer actions, while decreasing `tau` sharpens the distribution, i.e., the control input is likely to be represented by a single feasible integer across the entire control parameter space. Additionally, the probability vector is perturbed by Gumbel noise.\n",
    "\n",
    "For more information, see:  \n",
    "- [Gumbel-Softmax](https://arxiv.org/abs/1611.01144)  \n",
    "- [PyTorch Documentation: `torch.nn.functional.gumbel_softmax`](https://pytorch.org/docs/stable/generated/torch.nn.functional.gumbel_softmax.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "88909f42",
   "metadata": {},
   "outputs": [],
   "source": [
    "if STE_method == 'sigmoid':\n",
    "    def relaxed_round(x, slope=10.0): # differentiable nearest integer rounding via Sigmoid STE\n",
    "        backward = (x-torch.floor(x)-0.5) # fractional value with rounding threshold\n",
    "        return torch.round(x) + (torch.sigmoid(slope*backward) - torch.sigmoid(slope*backward).detach())\n",
    "           #  forward pass↑     backward pass↑                                               no grad↑\n",
    "    round_fn = lambda u_c, u_delta_relaxed: torch.cat((u_c, torch.clip(relaxed_round(u_delta_relaxed),u_delta_min,u_delta_max)), dim=-1)\n",
    "\n",
    "elif STE_method == 'softmax':\n",
    "    tau = 2 # temperature coefficient in (0,inf)\n",
    "    round_fn = lambda u_c, u_delta_relaxed: torch.cat((u_c, gumbel_softmax(u_delta_relaxed, tau=tau, hard=True) @ integers.T), dim=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c1e7a1e",
   "metadata": {},
   "source": [
    "## Differentiable computational graph components\n",
    "\n",
    "We leverage straight-through estimator paradigm to find differentiable surrogates of rounding and argmax functions. With rounding we leverage sigmoid gradients, while for categorical formulation, we leverage derivatives of softmax function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a332ae11",
   "metadata": {},
   "outputs": [],
   "source": [
    "dynamics_node = Node(dynamics, ['x','u','d'], ['x'], name='dynamics_model') # system dynamics\n",
    "\n",
    "continous_policy_node = Node(net_continous, ['x','r','d'], ['u_c'], name='continous_input_policy')\n",
    "integer_policy_node = Node(net_integer, ['x','r','d'], ['u_delta'], name='integer_input_policy')\n",
    "rounding_node = Node(round_fn, input_keys=['u_c', 'u_delta'], output_keys=['u'], name='soft_rounding')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba75b94c",
   "metadata": {},
   "source": [
    "## Computational graph as a closed-loop system\n",
    "\n",
    "The computational graph is composed of two neural policy nodes, straight-through rounding node, and system dynamics. Note that we perform future preview of references and disturbance for both neural policy modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b1169c26",
   "metadata": {},
   "outputs": [],
   "source": [
    "cl_system = SystemPreview([continous_policy_node, integer_policy_node, rounding_node, dynamics_node], # computational graph\n",
    "                            nsteps=nsteps, name='cl_system', pad_mode='reflect',\n",
    "                            preview_keys_map={'r': ['continous_input_policy', 'integer_input_policy'],  # preview references for both control policy modules\n",
    "                                            'd': ['continous_input_policy', 'integer_input_policy']} )# preview disturbance for both control policy modules"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "785c6064",
   "metadata": {},
   "source": [
    "## Generating synthetic dataset\n",
    "Training dataset covers an expected probability distribution of the control parameters $\\xi_k \\sim P_{ \\xi}$. Note that $x\\sim\\mathcal{U}(x_\\text{min},x_{max})$, $x\\sim\\beta(0.2,1.4)$, while references are sampled as periodic signals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "519ab7d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_data, batch_size = 14000, 2000\n",
    "# sample disturbanecs\n",
    "dist_magnitude = 5\n",
    "dist_data = np.random.beta(0.2, 1.4, (num_data*(nsteps+1)))*dist_magnitude\n",
    "dist_data_torch = torch.tensor(dist_data, dtype=torch.get_default_dtype()).reshape(num_data,nsteps+1,nd)\n",
    "# sample references\n",
    "ref1_baseline, ref2_baseline = 4, 2 # reference baseline\n",
    "ref1_data = ref1_baseline + 1.5*np.sin(0.05*np.arange(num_data*(nsteps+1))).reshape(-1,1)\n",
    "ref2_data = ref2_baseline + 0.5*np.cos(0.05*np.arange(num_data*(nsteps+1))).reshape(-1,1)\n",
    "ref_data = np.concatenate((ref1_data, ref2_data), axis=-1)\n",
    "ref_data_torch = torch.tensor(ref_data, dtype=torch.get_default_dtype()).reshape(num_data,nsteps+1,nref)\n",
    "# sample initial conditions\n",
    "x1_data = torch.empty(num_data, 1, 1).uniform_(x1_min, x1_max)\n",
    "x2_data = torch.empty(num_data, 1, 1).uniform_(x2_min, x2_max)\n",
    "x_data = torch.cat((x1_data, x2_data), dim=-1)\n",
    "\n",
    "num_train_data = 10000 # training data splits to train data and development data\n",
    "\n",
    "train_data = DictDataset({'x': x_data[:num_train_data], 'd': dist_data_torch[:num_train_data], 'r': ref_data_torch[:num_train_data]}, name='train')  # Split conditions into train and dev\n",
    "dev_data = DictDataset({'x': x_data[num_train_data:], 'd': dist_data_torch[num_train_data:], 'r': ref_data_torch[num_train_data:]}, name='dev')\n",
    "# instantiate data loaders\n",
    "train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, collate_fn=train_data.collate_fn)\n",
    "dev_loader = torch.utils.data.DataLoader(dev_data, batch_size=batch_size, collate_fn=dev_data.collate_fn)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc250ab4",
   "metadata": {},
   "source": [
    "## Control objectives\n",
    "\n",
    "We define control objectives and process constraints using NeuroMANCER's symbolic variables. Our control objectives include reference tracking $\\!\\|x_k\\!-\\!r_k\\|_Q^2$ and control input smoothing $\\|u_{k+1}-u_{k}\\|_R^2$, $ \\|\\delta_{k+1}-\\delta_{k}\\|^2_R$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fb396446",
   "metadata": {},
   "outputs": [],
   "source": [
    "u = variable('u') # control inputs\n",
    "x = variable('x') # system states\n",
    "r = variable('r') # referece\n",
    "# control objectives\n",
    "Q_control, R_smoothing = 10., 1. # reference tracking and control smoothing weights\n",
    "tracking_loss = Q_control * ((x == r)^2)     # reference tracking loss\n",
    "input_smoothing_loss = R_smoothing * ((u[:, 1:, :] == u[:, :-1, :])^2) # control smoothing loss\n",
    "tracking_loss.name, input_smoothing_loss.name = 'tracking_loss', 'smoothing_loss'\n",
    "# constraints\n",
    "x1_lb, x1_ub = 50.*(x[:,:, [0]] >= x1_min), 50.*(x[:,:, [0]] <= x1_max)\n",
    "x2_lb, x2_ub = 50.*(x[:,:, [1]] >= x2_min), 50.*(x[:,:, [1]] <= x2_max)\n",
    "u_c_lb, u_c_ub = 50.*(u[:,:, [0]] >= u_c_min), 50.*(u[:,:, [0]] <= u_c_max)\n",
    "x1_lb.name, x1_ub.name = 'x1_lb', 'x1_ub'\n",
    "x2_lb.name, x2_ub.name = 'x2_lb', 'x2_ub'\n",
    "u_c_lb.name, u_c_ub.name = 'u_lb', 'u_ub' \n",
    "\n",
    "constraints = [x1_lb, x1_ub, x2_lb, x2_ub, u_c_lb, u_c_ub]\n",
    "loss = PenaltyLoss([tracking_loss, input_smoothing_loss], constraints)\n",
    "problem = Problem([cl_system], loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eed41f72",
   "metadata": {},
   "source": [
    "## Optimization parameters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "044694b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0  train_loss: 36.310001373291016\n",
      "epoch: 1  train_loss: 30.085494995117188\n",
      "epoch: 2  train_loss: 27.66848373413086\n",
      "epoch: 3  train_loss: 26.737384796142578\n",
      "epoch: 4  train_loss: 23.487178802490234\n",
      "epoch: 5  train_loss: 21.428329467773438\n",
      "epoch: 6  train_loss: 20.01283836364746\n",
      "epoch: 7  train_loss: 18.32138442993164\n",
      "epoch: 8  train_loss: 16.701679229736328\n",
      "epoch: 9  train_loss: 15.632085800170898\n",
      "epoch: 10  train_loss: 14.979771614074707\n",
      "epoch: 11  train_loss: 14.302393913269043\n",
      "epoch: 12  train_loss: 13.545145034790039\n",
      "epoch: 13  train_loss: 12.89532470703125\n",
      "epoch: 14  train_loss: 12.389701843261719\n",
      "epoch: 15  train_loss: 11.93684196472168\n",
      "epoch: 16  train_loss: 11.533403396606445\n",
      "epoch: 17  train_loss: 11.16711139678955\n",
      "epoch: 18  train_loss: 10.831823348999023\n",
      "epoch: 19  train_loss: 10.491620063781738\n",
      "epoch: 20  train_loss: 10.155242919921875\n",
      "epoch: 21  train_loss: 9.82606029510498\n",
      "epoch: 22  train_loss: 9.505916595458984\n",
      "epoch: 23  train_loss: 9.207271575927734\n",
      "epoch: 24  train_loss: 8.946222305297852\n",
      "epoch: 25  train_loss: 8.728181838989258\n",
      "epoch: 26  train_loss: 8.536822319030762\n",
      "epoch: 27  train_loss: 8.360223770141602\n",
      "epoch: 28  train_loss: 8.197542190551758\n",
      "epoch: 29  train_loss: 8.050979614257812\n",
      "epoch: 30  train_loss: 7.917119026184082\n",
      "epoch: 31  train_loss: 7.793443202972412\n",
      "epoch: 32  train_loss: 7.678706169128418\n",
      "epoch: 33  train_loss: 7.5699872970581055\n",
      "epoch: 34  train_loss: 7.4687066078186035\n",
      "epoch: 35  train_loss: 7.372979164123535\n",
      "epoch: 36  train_loss: 7.2833123207092285\n",
      "epoch: 37  train_loss: 7.200344085693359\n",
      "epoch: 38  train_loss: 7.12246036529541\n",
      "epoch: 39  train_loss: 7.050304412841797\n",
      "epoch: 40  train_loss: 6.983426570892334\n",
      "epoch: 41  train_loss: 6.920546054840088\n",
      "epoch: 42  train_loss: 6.861664772033691\n",
      "epoch: 43  train_loss: 6.805779933929443\n",
      "epoch: 44  train_loss: 6.753551483154297\n",
      "epoch: 45  train_loss: 6.7037248611450195\n",
      "epoch: 46  train_loss: 6.65664529800415\n",
      "epoch: 47  train_loss: 6.613452911376953\n",
      "epoch: 48  train_loss: 6.5718674659729\n",
      "epoch: 49  train_loss: 6.532630920410156\n",
      "epoch: 50  train_loss: 6.495736598968506\n",
      "epoch: 51  train_loss: 6.459705352783203\n",
      "epoch: 52  train_loss: 6.426279544830322\n",
      "epoch: 53  train_loss: 6.3947529792785645\n",
      "epoch: 54  train_loss: 6.363831520080566\n",
      "epoch: 55  train_loss: 6.335240364074707\n",
      "epoch: 56  train_loss: 6.307772636413574\n",
      "epoch: 57  train_loss: 6.281436920166016\n",
      "epoch: 58  train_loss: 6.256314754486084\n",
      "epoch: 59  train_loss: 6.232592582702637\n",
      "epoch: 60  train_loss: 6.209870338439941\n",
      "epoch: 61  train_loss: 6.18853235244751\n",
      "epoch: 62  train_loss: 6.168488502502441\n",
      "epoch: 63  train_loss: 6.14885950088501\n",
      "epoch: 64  train_loss: 6.130214691162109\n",
      "epoch: 65  train_loss: 6.112023830413818\n",
      "epoch: 66  train_loss: 6.094650745391846\n",
      "epoch: 67  train_loss: 6.078188419342041\n",
      "epoch: 68  train_loss: 6.0627121925354\n",
      "epoch: 69  train_loss: 6.048051357269287\n",
      "epoch: 70  train_loss: 6.033275604248047\n",
      "epoch: 71  train_loss: 6.0195631980896\n",
      "epoch: 72  train_loss: 6.0062761306762695\n",
      "epoch: 73  train_loss: 5.993493556976318\n",
      "epoch: 74  train_loss: 5.981596946716309\n",
      "epoch: 75  train_loss: 5.969497203826904\n",
      "epoch: 76  train_loss: 5.958029270172119\n",
      "epoch: 77  train_loss: 5.947490215301514\n",
      "epoch: 78  train_loss: 5.936966896057129\n",
      "epoch: 79  train_loss: 5.926796913146973\n",
      "epoch: 80  train_loss: 5.91706657409668\n",
      "epoch: 81  train_loss: 5.907535076141357\n",
      "epoch: 82  train_loss: 5.898428916931152\n",
      "epoch: 83  train_loss: 5.889498710632324\n",
      "epoch: 84  train_loss: 5.880975723266602\n",
      "epoch: 85  train_loss: 5.872587203979492\n",
      "epoch: 86  train_loss: 5.864660263061523\n",
      "epoch: 87  train_loss: 5.857085227966309\n",
      "epoch: 88  train_loss: 5.849879741668701\n",
      "epoch: 89  train_loss: 5.842681407928467\n",
      "epoch: 90  train_loss: 5.835326194763184\n",
      "epoch: 91  train_loss: 5.828362464904785\n",
      "epoch: 92  train_loss: 5.821643829345703\n",
      "epoch: 93  train_loss: 5.814966201782227\n",
      "epoch: 94  train_loss: 5.808263301849365\n",
      "epoch: 95  train_loss: 5.802028656005859\n",
      "epoch: 96  train_loss: 5.7959303855896\n",
      "epoch: 97  train_loss: 5.78996467590332\n",
      "epoch: 98  train_loss: 5.784875869750977\n",
      "epoch: 99  train_loss: 5.779355525970459\n",
      "epoch: 100  train_loss: 5.774395942687988\n",
      "epoch: 101  train_loss: 5.769095420837402\n",
      "epoch: 102  train_loss: 5.763983726501465\n",
      "epoch: 103  train_loss: 5.758888244628906\n",
      "epoch: 104  train_loss: 5.753945350646973\n",
      "epoch: 105  train_loss: 5.749325752258301\n",
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      "epoch: 107  train_loss: 5.740832805633545\n",
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      "epoch: 109  train_loss: 5.732736110687256\n",
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      "epoch: 111  train_loss: 5.72477388381958\n",
      "epoch: 112  train_loss: 5.721251487731934\n",
      "epoch: 113  train_loss: 5.717962265014648\n",
      "epoch: 114  train_loss: 5.7149128913879395\n",
      "epoch: 115  train_loss: 5.711711406707764\n",
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      "epoch: 117  train_loss: 5.705226898193359\n",
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      "epoch: 158  train_loss: 5.633384704589844\n",
      "epoch: 159  train_loss: 5.632505893707275\n",
      "epoch: 160  train_loss: 5.631453037261963\n",
      "epoch: 161  train_loss: 5.630850791931152\n",
      "epoch: 162  train_loss: 5.629999160766602\n",
      "epoch: 163  train_loss: 5.6292619705200195\n",
      "epoch: 164  train_loss: 5.628751277923584\n",
      "epoch: 165  train_loss: 5.627751350402832\n",
      "epoch: 166  train_loss: 5.626885414123535\n",
      "epoch: 167  train_loss: 5.626126289367676\n",
      "epoch: 168  train_loss: 5.625430107116699\n",
      "epoch: 169  train_loss: 5.624688625335693\n",
      "epoch: 170  train_loss: 5.623953342437744\n",
      "epoch: 171  train_loss: 5.623352527618408\n",
      "epoch: 172  train_loss: 5.622572422027588\n",
      "epoch: 173  train_loss: 5.621821880340576\n",
      "epoch: 174  train_loss: 5.621223449707031\n",
      "epoch: 175  train_loss: 5.620822906494141\n",
      "epoch: 176  train_loss: 5.620203971862793\n",
      "epoch: 177  train_loss: 5.619742393493652\n",
      "epoch: 178  train_loss: 5.619217872619629\n",
      "epoch: 179  train_loss: 5.618749141693115\n",
      "epoch: 180  train_loss: 5.618143558502197\n",
      "epoch: 181  train_loss: 5.61748743057251\n",
      "epoch: 182  train_loss: 5.616896629333496\n",
      "epoch: 183  train_loss: 5.616494655609131\n",
      "epoch: 184  train_loss: 5.615996360778809\n",
      "epoch: 185  train_loss: 5.6155314445495605\n",
      "epoch: 186  train_loss: 5.614951133728027\n",
      "epoch: 187  train_loss: 5.614492893218994\n",
      "epoch: 188  train_loss: 5.614139080047607\n",
      "epoch: 189  train_loss: 5.6136603355407715\n",
      "epoch: 190  train_loss: 5.613406658172607\n",
      "epoch: 191  train_loss: 5.612832069396973\n",
      "epoch: 192  train_loss: 5.612774848937988\n",
      "epoch: 193  train_loss: 5.612426280975342\n",
      "epoch: 194  train_loss: 5.611882209777832\n",
      "epoch: 195  train_loss: 5.61154842376709\n",
      "epoch: 196  train_loss: 5.610907554626465\n",
      "epoch: 197  train_loss: 5.610569000244141\n",
      "epoch: 198  train_loss: 5.610227108001709\n",
      "epoch: 199  train_loss: 5.610015869140625\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimizer = torch.optim.Adam(cl_system.parameters(), lr=0.0003, amsgrad=False, weight_decay=0.)\n",
    "trainer = Trainer(\n",
    "    problem,\n",
    "    train_loader, dev_loader,\n",
    "    optimizer=optimizer,\n",
    "    epochs=200,\n",
    "    train_metric='train_loss',\n",
    "    dev_metric='dev_loss',\n",
    "    eval_metric='dev_loss',\n",
    "    warmup=20,\n",
    "    patience=80,\n",
    ")\n",
    "best_model = trainer.train()    # start optimization\n",
    "trainer.model.load_state_dict(best_model) # load best trained model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2e206e8",
   "metadata": {},
   "source": [
    "## Control policy test\n",
    "\n",
    "Note that for testing, disturbance and reference signals are sampled from slightly different distributions than in training. Including this unceirtanty, we test generalizability of the trained control policy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a74b7d7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "s_length = 100 # number of simulation steps\n",
    "dists = np.random.beta(0.2, 1., (s_length+nsteps))*dist_magnitude # disturbances \n",
    "dists_torch = torch.tensor(dists, requires_grad=False, dtype=torch.get_default_dtype()).reshape(1,-1,nd)\n",
    "ref1 = ref1_baseline + 1.*np.sin(0.1*np.arange(s_length+nsteps)).reshape(-1,1) # references\n",
    "ref2 = ref2_baseline + 1.*np.cos(0.1*np.arange(s_length+nsteps)).reshape(-1,1)\n",
    "refs = np.concatenate((ref1,ref2),axis=-1)\n",
    "refs_torch = torch.tensor(refs, requires_grad=False, dtype=torch.get_default_dtype()).reshape(1,-1,nref)\n",
    "\n",
    "sim_data = {'x': 3*torch.rand(1, 1, nx),\n",
    "        'r': refs_torch,\n",
    "        'd': dists_torch}\n",
    "\n",
    "cl_system.nsteps = s_length\n",
    "with torch.no_grad():\n",
    "    trajectories = cl_system(sim_data) # simulate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd60dc0d",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "34c58a5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bold914\\AppData\\Local\\Temp\\ipykernel_22040\\1791729733.py:10: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "  [ ( i.grid(), i.set_xlabel(\"time\"), i.legend()) for i in ax.flat ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,2, figsize=(14,7))\n",
    "ax[0,0].axhline(x1_min, linestyle=':', c='k', label='bounds'); ax[0,0].axhline(x1_max, linestyle=':', c='k')\n",
    "ax[0,0].plot(trajectories['r'][0,:s_length,0], 'k--', label='reference'); ax[0,0].plot(trajectories['x'][0,:,0], label='process'); \n",
    "ax[1,0].plot(trajectories['x'][0,:,1]); ax[1,0].plot(trajectories['r'][0,:s_length,1], 'k--') \n",
    "[ax[1,0].axhline(i, linestyle=':', c='k') for i in [x2_min, x2_max]]\n",
    "[ax[0,1].axhline(i, linestyle=':', c='k') for i in [u_c_min, u_c_max]]; ax[0,1].plot(trajectories['u'][0,:,0]); \n",
    "[ax[1,1].axhline(i, linestyle=':', c='k') for i in [u_delta_min, u_delta_max]]; \n",
    "ax[1,1].step(np.arange(0,len(trajectories['u'][0,:,1])),trajectories['u'][0,:,1], linewidth=1.5)\n",
    "\n",
    "[ ( i.grid(), i.set_xlabel(\"time\"), i.legend()) for i in ax.flat ]\n",
    "ax[0,0].set_ylabel('$x_1$'); ax[1,0].set_ylabel('$x_2$'); ax[0,1].set_ylabel('continous input'); ax[1,1].set_ylabel('integer input')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(7,3)); plt.plot(trajectories['d'][0,:s_length,0]); \n",
    "plt.ylabel('disturbances'); plt.xlabel('time'); plt.grid()\n",
    "plt.show()"
   ]
  },
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   "id": "1b9a7055",
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   "source": [
    "## Conclusions\n",
    "\n",
    "Based on the simulation roullout, we may observe that the trained control policy effectively tracks the dynamic reference for both states while respecting state, continuous input, and integrality constraints. Slight deviations of the states from the references can be observed, which are caused by differences in the probability distributions of the testing and training datasets for reference and disturbance signals. Despite this, the explicit control policies generalize well under the given conditions.\n",
    "\n",
    "#### Note:\n",
    "The volatility of the integer control input trajectory with `gumbel_softmax` can be reduced by disabling the Gumbel noise during inference. This step was omitted in the notebook for the sake of code simplicity.\n"
   ]
  }
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